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Question Answering
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/luozhilin99/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lupantech/MathVista
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
https://github.com/lupantech/chameleon-llm
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/lurosenb/sass
https://github.com/luyug/GC-DPR
https://github.com/lvyufeng/Cybertron/blob/master/cybertron/models/bert.py
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lvyufeng/ERNIE_mindspore
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/lvyufeng/bert4ms
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lvyufeng/bert4ms/blob/master/bert4ms/models/xlnet.py
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/lvyufeng/mindspore-nlp-tutorial
https://github.com/lx709/reobench
VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
https://github.com/lyan62/data-curation
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/lyccol/CodeBERT-based-webshell-detection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lyqcom/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lyqcom/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/lyqcom/mindspore_nlp_tutorial
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/lywang3081/caf
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One
https://github.com/lzw-lzw/awesome-remote-sensing-vision-language-models
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/m3yrin/naqanet_notebook
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
https://github.com/m42-health/med42
Towards Expert-Level Medical Question Answering with Large Language Models
https://github.com/maanvithag/thinkai
https://github.com/macco3k/deepstories
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/madhawav/MML
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/magahub/songrnn
https://github.com/majing2019/transformer
https://github.com/makarandtapaswi/MovieQA_benchmark
MovieQA: Understanding Stories in Movies through Question-Answering
https://github.com/makcedward/nlpaug
Adversarial Examples for Evaluating Reading Comprehension Systems
https://github.com/maknotavailable/pytorch-pretrained-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mali-git/CSQA_Implementation
https://github.com/malllabiisc/EmbedKGQA
https://github.com/mandarjoshi90/coref
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/mandubian/codenets
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/mandubian/pytorch_math_dataset
https://github.com/mangopy/direct-rag-learning
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.